An Delin, Wang Chaoli
IEEE Trans Vis Comput Graph. 2025 Jun;31(6):3771-3782. doi: 10.1109/TVCG.2025.3567133.
Unlike their line-based counterparts, surface-based techniques have yet to be thoroughly investigated in flow visualization due to their significant placement, speed, perception, and evaluation challenges. This article presents SurfPatch, a novel framework supporting exploratory stream surface visualization. To begin with, we translate the issue of surface placement to surface selection and trace a large number of stream surfaces from a given flow field dataset. Then, we introduce a three-stage process: vertex-level classification, patch-level matching, and surface-level clustering that hierarchically builds the connection between vertices and patches and between patches and surfaces. This bottom-up approach enables fine-grained, multiscale patch-level matching, sharply contrasts surface-level matching offered by existing works, and provides previously unavailable flexibility during querying. We design an intuitive visual interface for users to conveniently visualize and analyze the underlying collection of stream surfaces in an exploratory manner. SurfPatch is not limited to stream surfaces traced from steady flow datasets. We demonstrate its effectiveness through experiments on stream surfaces produced from steady and unsteady flows as well as isosurfaces extracted from scalar fields.
与基于线条的同类技术不同，基于曲面的技术由于在放置、速度、感知和评估方面存在重大挑战，尚未在流动可视化中得到充分研究。本文介绍了SurfPatch，这是一个支持探索性流曲面可视化的新颖框架。首先，我们将曲面放置问题转化为曲面选择问题，并从给定的流场数据集中追踪大量的流曲面。然后，我们引入了一个三阶段过程：顶点级分类、面片级匹配和曲面级聚类，该过程分层构建顶点与面片之间以及面片与曲面之间的连接。这种自下而上的方法实现了细粒度、多尺度的面片级匹配，与现有工作提供的曲面级匹配形成鲜明对比，并在查询期间提供了前所未有的灵活性。我们为用户设计了一个直观的视觉界面，以便以探索性方式方便地可视化和分析流曲面的底层集合。SurfPatch不限于从稳定流数据集追踪的流曲面。我们通过对由稳定和不稳定流产生的流曲面以及从标量场提取的等值面进行实验，证明了它的有效性。
